The present disclosure relates, generally, to systems and method for processing optical images. More particularly, the disclosure relates to automatic detection of polyps in optical images.
Colonoscopy is the preferred technique for colon cancer screening and prevention, during which a tiny camera is inserted and guided through the colon to detect and remove polyps—precursors to colon cancer. However, a colonoscopy is an operator dependent procedure, wherein human factors, such as fatigue and insufficient attentiveness during colon examination, particularly during back-to-back procedures, can lead to the miss detection of polyps. By some estimates the average polyp miss-rate is between 4 and 12%. Patients with missed polyps may be diagnosed with a late stage cancer with the survival rate of less than 10%.
Computer-aided polyp detection has been a promising approach to reducing polyp miss-rate and encouraging attentiveness during procedures. However, automatic polyp detection remains a challenging task. In particular, shapes of polyps can vary considerably, with the same polyp appearing differently depending on the viewing angle of the colonoscopy camera and spontaneous spasms of the colon. In addition, polyp texture becomes fully visible only if a given polyp appears within the depth of field of the camera. Considering that many cameras have non-adjustable depth of fields, making texture availability dependent on the distance between the polyp and camera. Furthermore, polyp color can vary depending on lighting conditions, appearing in different shades, ranging from dark to saturated colors.
Early works employed color and texture features detected in colonoscopy images to identify polyps. For instance, the work based on color wavelet features sets a representative example. However, the effectiveness of such methods is limited by partial texture visibility of polyps during a colonoscopy procedure, as well as large color variations among polyps. More recent techniques have considered shape, spatio-temporal, and appearance features. Specifically, some groups have attempted use of elliptical-shaped features, while others have employed valley information to localize polyps. However, geometric features in the absence of contextual clues can be misleading, while valley information may result in false detections particularly around wrinkles and vascular structures. Moreover, spatio-temporal features are only suitable for off-line processing of colonoscopy videos, given that such methods require information from the past and future frames for polyp localization at a current frame.
Consequently, considering the limitations of previous technological approaches, it would be desirable to have a system and method for accurate and reliable polyp detection in optical colonoscopy images in real time.